Student modeling for a web-based learning environment: a data mining approach
Eighteenth national conference on Artificial intelligence
E-learning personalization based on itineraries and long-term navigational behavior
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
Analysing users' access logs in Moodle to improve e learning
EATIS '07 Proceedings of the 2007 Euro American conference on Telematics and information systems
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We present an application of data mining in e-learning, where web platform management was supported by the extraction of users' activity features and further by the clusterisation of users' profiles By this approach we have identified groups of users with a similar activity on e-learning platform and were able to observe their performance The experiments presented in this paper were performed on the real data coming from Moodle platform Comparing to the other research in this filed, that focus on the analysis of students, we investigated teachers' behaviour We have proposed a smoothing model in the form of a dynamic system, that was used to transform the logged events into time series of activities These series were later used to cluster teachers' performance and to divide them into three groups: active, moderate and passive users The main aim of our research was to propose and test an data mining based approach to support of e-learning management by an observation of teachers leading to an increase of the process quality.